Multi-Visual Camera Registration for 3D Obstacle Detection
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Solution Overview
Problem
Current systems for controlling industrial and agricultural machinery lack efficient methods for obtaining, processing, and implementing environmental and target subject information, leading to inefficiencies and potential damage due to obstacles, especially in operator-based and autonomous/semi-autonomous systems.
Innovation Solution
An intelligent multi-visual camera system with multiple cameras mounted on a support frame, capable of registering images into a three-dimensional volume, detecting features of interest, and communicating this information to actuators or control systems, enhancing computational speed and accuracy through tracking and deduplication operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If multiple visual cameras are used to sense over a large region, then the coverage area and object detection capability are improved, but the device complexity and computational processing requirements increase
Solution Approach 1:
The system divides the coverage area into multiple zones, each monitored by a dedicated camera. The processor segments the computational task by assigning different processing algorithms to different camera feeds based on their specific detection needs, thereby managing complexity through systematic division of labor
Solution Approach 2:
The system transitions from two-dimensional image data to three-dimensional spatial understanding by integrating multiple camera perspectives. This dimensional transformation enables comprehensive environmental mapping and obstacle detection while the processor manages the complexity through structured 3D coordinate system construction
2Measurement precision
If multiple visual cameras are used to maintain image quality over a large region, then the measurement precision and object detection accuracy are improved, but the device complexity and data processing demands increase
Solution Approach 1:
The system merges data from multiple cameras to create a unified high-resolution environmental model. The processor combines images from different cameras to enhance detection accuracy for objects in shared fields of view, while managing complexity through intelligent data fusion algorithms that process only relevant features
Solution Approach 2:
The processor is designed with multi-functional capabilities to handle various camera feeds simultaneously using the same core detection algorithms. This universal processing approach maintains consistency across different camera inputs while reducing overall system complexity through standardized treatment of diverse data sources
3Loss of information
If visual information is used to distinguish obstacles, targets, and other objects, then the information richness and object classification capability are improved, but the computational processing requirements and time increase
Solution Approach 1:
The system performs preliminary classification of detected objects into categories (obstacles, targets, neutral objects) using automated image recognition algorithms. This preliminary action enables the control system to prioritize processing of critical objects while maintaining information richness, thereby reducing overall processing time without sacrificing detection accuracy
Solution Approach 2:
The system implements feedback loops where detection results from previous time steps inform processing priorities in current time steps. Previously identified objects and patterns are used to guide current detection efforts, allowing the system to maintain high information richness while reducing redundant processing and minimizing time loss
4Productivity
If computer-based sensing is used to process and react to objects of interest, then the operational efficiency and response speed are improved, but the computational demands and energy consumption increase
Solution Approach 1:
The system applies partial processing to camera feeds based on detected relevance. When objects of interest are detected in certain regions, the processor intensifies analysis in those specific areas while reducing processing intensity in regions without significant targets. This selective approach maintains high operational efficiency while minimizing overall computational energy consumption
Data Source
AI summary
An intelligent multi-visual camera system is disclosed that includes a multiple visual sensor array and a control system. The multiple visual sensor array includes multiple visual cameras spaced apart from each other. The control system also receives input from the multiple visual cameras, stores instructions that cause the processor to: initiate a registration system that projects images from the multiple visual cameras into a single three-dimensional volume and coordinate overlapping pixels of adjacent images amongst each other; detect one or more features of interest in the images from the multiple visual cameras; track one or more features of interest in one frame in one image from the multiple visual cameras into a subsequent frame in a subsequent image from the multiple visual cameras; deduplicate the projected images from the multiple visual cameras onto the ground plane; and communicate information regarding the features of interest that have been detected.